Protecting sensitive data using conversational history

ABSTRACT

Methods and systems for protecting sensitive data using conversational history are described herein. An enterprise data validation server may receive conversation snippets and create a topic model. The enterprise data validation server may detect a message is being sent from a first user to a second user, determine a topic distribution between the first user and the second user, and a topic distribution of the message. The enterprise data validation server may determine a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user. Accordingly, based on a determination that the bias value exceeds a threshold, the enterprise data validation server send an alert containing a warning message.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a bypass continuation of PCT/CN2020/138655 filed Dec. 23, 2020, and having the same title.

FIELD

Aspects described herein generally relate to computer networking, remote computer access, virtualization, enterprise mobility management, and hardware and software related thereto. More specifically, one or more aspects describe herein provide data management and data security in a cloud computing environment.

BACKGROUND

In a cloud-based environment, enterprise users may open multiple text-based conversation windows in various applications (e.g. workspace applications) on a user device. In some conventional systems, enterprise users may need to frequently switch across multiple conversation windows to communicate with different people involving different subjects. There may be a risk that a user may inadvertently send a message (or file attachment) to an unintended recipient. The situation may be exacerbated when the message contains sensitive information related to a finance or Human Resources (HR) department. As a result, conventional systems are unable to provide data management with adequate security, performance and optimal user experience.

SUMMARY

The following presents a simplified summary of various aspects described herein. This summary is not an extensive overview, and is not intended to identify required or critical elements or to delineate the scope of the claims. The following summary merely presents some concepts in a simplified form as an introductory prelude to the more detailed description provided below.

To overcome limitations in the prior art described above, and to overcome other limitations that will be apparent upon reading and understanding the present specification, aspects described herein are directed towards protecting sensitive data using conversational history.

In accordance with one or more aspects of the disclosure, an enterprise data validation server having at least one processor, memory, and a communication interface may receive, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment. Subsequently, the enterprise data validation server may create, based on the conversation snippets, a topic model using a machine learning model such as latent Dirichlet allocation (LDA) modeling. The enterprise data validation server may detect, via the communication interface, a message being sent from a first user to a second user. Thereafter, the enterprise data validation server may determine, based on the topic model, a topic distribution between the first user and the second user; and determine a topic distribution of the message. The enterprise data validation server may determine a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user. Accordingly, based on a determination that the bias value exceeds a threshold, the enterprise data validation server may send an alert to the first user to prevent the message from being sent to the second user.

In some instances, prior to receiving the conversation snippets, the enterprise data validation server may capture HTTP message associated with the conversation snippets from a browser of an application.

In some instances, prior to receiving the conversation snippets, the enterprise data validation server may capture the conversation snippets from native codes of an application.

In some instances, after receiving the conversation snippets, the enterprise data validation server may store the conversation snippets in a corpus database.

In some instances, after creating the topic model, the enterprise data validation server may generate the topic distribution associated with conversations associated with a plurality of users; and store the topic distribution in a topic distribution database.

In some instances, the enterprise data validation server may use the topic distribution and a topic number as inputs for the LDA modeling, and tune the topic model to identify an appropriate topic number.

In some instances, the enterprise data validation server may apply the topic model to a production environment, and tune the topic model based on user feedback.

In some instances, the enterprise data validation server may adjust the bias value based on sensitive information being in the message.

In some instances, the enterprise data validation server may send the alert indicating that the sensitive information in the message is to be disclosed to the second user, and block the message from being sent to the second user based on a negative feedback from the first user.

In some instances, the enterprise data validation server may send the alert indicating that the sensitive information in the message is to be disclosed to the second user, and permit the message being sent to the second user based on a positive feedback from the first user.

These and additional aspects will be appreciated with the benefit of the disclosures discussed in further detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of aspects described herein and the advantages thereof may be acquired by referring to the following description in consideration of the accompanying drawings, in which like reference numbers indicate like features, and wherein:

FIG. 1 depicts an illustrative computer system architecture that may be used in accordance with one or more illustrative aspects described herein.

FIG. 2 depicts an illustrative remote-access system architecture that may be used in accordance with one or more illustrative aspects described herein.

FIG. 3 depicts an illustrative virtualized system architecture that may be used in accordance with one or more illustrative aspects described herein.

FIG. 4 depicts an illustrative cloud-based system architecture that may be used in accordance with one or more illustrative aspects described herein.

FIG. 5 depicts an illustrative enterprise mobility management system.

FIG. 6 depicts another illustrative enterprise mobility management system.

FIG. 7 depicts an illustrative computing environment for protecting sensitive data using conversational history in accordance with one or more illustrative aspects described herein.

FIG. 8 depicts illustrative conversation snippets in accordance with one or more illustrative aspects described herein.

FIGS. 9A-9C depict an example event sequence for protecting sensitive data using conversational history, in accordance with one or more illustrative aspects described herein.

FIG. 10 depicts an example algorithm of for protecting sensitive data using conversational history in accordance with one or more illustrative aspects described herein.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference is made to the accompanying drawings identified above and which form a part hereof, and in which is shown by way of illustration various embodiments in which aspects described herein may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope described herein. Various aspects are capable of other embodiments and of being practiced or being carried out in various different ways.

As a general introduction to the subject matter described in more detail below, aspects described herein are directed towards protecting sensitive data using conversational history. An enterprise data validation server may automatically capture conversational snippets from various communication and collaboration tools in a virtual workspace, such as Citrix Workspace, Microsoft Teams, Skype and Google Workspace. Based on the conversation snippets, the enterprise data validation server may create a topic model. The enterprise data validation server may further detect that a message is being sent from a first user to a second user in the virtual workspace and use the topic model to determine a topic distribution between the first user and the second user. The enterprise data validation server may also determine a topic distribution of the message and a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user. If the bias value exceeds a predetermined threshold, indicating that the sensitive information may not be related to a previous conversational history with the second user, the enterprise data validation server may send an alert to the first user asking the first user to confirm the recipient of the message. As such, the enterprise data validation server may reduce the risks of inadvertently sending sensitive information to unintended recipients, enhance data security and facilitate communications among users.

It is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. Rather, the phrases and terms used herein are to be given their broadest interpretation and meaning. The use of “including” and “comprising” and variations thereof is meant to encompass the items listed thereafter and equivalents thereof as well as additional items and equivalents thereof. The use of the terms “mounted,” “connected,” “coupled,” “positioned,” “engaged” and similar terms, is meant to include both direct and indirect mounting, connecting, coupling, positioning and engaging.

Computing Architecture

Computer software, hardware, and networks may be utilized in a variety of different system environments, including standalone, networked, remote-access (also known as remote desktop), virtualized, and/or cloud-based environments, among others. FIG. 1 illustrates one example of a system architecture and data processing device that may be used to implement one or more illustrative aspects described herein in a standalone and/or networked environment. Various network nodes 103, 105, 107, and 109 may be interconnected via a wide area network (WAN) 101, such as the Internet. Other networks may also or alternatively be used, including private intranets, corporate networks, local area networks (LAN), metropolitan area networks (MAN), wireless networks, personal networks (PAN), and the like. Network 101 is for illustration purposes and may be replaced with fewer or additional computer networks. A local area network 133 may have one or more of any known LAN topology and may use one or more of a variety of different protocols, such as Ethernet. Devices 103, 105, 107, and 109 and other devices (not shown) may be connected to one or more of the networks via twisted pair wires, coaxial cable, fiber optics, radio waves, or other communication media.

The term “network” as used herein and depicted in the drawings refers not only to systems in which remote storage devices are coupled together via one or more communication paths, but also to stand-alone devices that may be coupled, from time to time, to such systems that have storage capability. Consequently, the term “network” includes not only a “physical network” but also a “content network,” which is comprised of the data—attributable to a single entity—which resides across all physical networks.

The components may include data server 103, web server 105, and client computers 107, 109. Data server 103 provides overall access, control and administration of databases and control software for performing one or more illustrative aspects describe herein. Data server 103 may be connected to web server 105 through which users interact with and obtain data as requested. Alternatively, data server 103 may act as a web server itself and be directly connected to the Internet. Data server 103 may be connected to web server 105 through the local area network 133, the wide area network 101 (e.g., the Internet), via direct or indirect connection, or via some other network. Users may interact with the data server 103 using remote computers 107, 109, e.g., using a web browser to connect to the data server 103 via one or more externally exposed web sites hosted by web server 105. Client computers 107, 109 may be used in concert with data server 103 to access data stored therein, or may be used for other purposes. For example, from client device 107 a user may access web server 105 using an Internet browser, as is known in the art, or by executing a software application that communicates with web server 105 and/or data server 103 over a computer network (such as the Internet).

Servers and applications may be combined on the same physical machines, and retain separate virtual or logical addresses, or may reside on separate physical machines. FIG. 1 illustrates just one example of a network architecture that may be used, and those of skill in the art will appreciate that the specific network architecture and data processing devices used may vary, and are secondary to the functionality that they provide, as further described herein. For example, services provided by web server 105 and data server 103 may be combined on a single server.

Each component 103, 105, 107, 109 may be any type of known computer, server, or data processing device. Data server 103, e.g., may include a processor 111 controlling overall operation of the data server 103. Data server 103 may further include random access memory (RAM) 113, read only memory (ROM) 115, network interface 117, input/output interfaces 119 (e.g., keyboard, mouse, display, printer, etc.), and memory 121. Input/output (I/O) 119 may include a variety of interface units and drives for reading, writing, displaying, and/or printing data or files. Memory 121 may further store operating system software 123 for controlling overall operation of the data processing device 103, control logic 125 for instructing data server 103 to perform aspects described herein, and other application software 127 providing secondary, support, and/or other functionality which may or might not be used in conjunction with aspects described herein. The control logic 125 may also be referred to herein as the data server software 125. Functionality of the data server software 125 may refer to operations or decisions made automatically based on rules coded into the control logic 125, made manually by a user providing input into the system, and/or a combination of automatic processing based on user input (e.g., queries, data updates, etc.).

Memory 121 may also store data used in performance of one or more aspects described herein, including a first database 129 and a second database 131. In some embodiments, the first database 129 may include the second database 131 (e.g., as a separate table, report, etc.). That is, the information can be stored in a single database, or separated into different logical, virtual, or physical databases, depending on system design. Devices 105, 107, and 109 may have similar or different architecture as described with respect to device 103. Those of skill in the art will appreciate that the functionality of data processing device 103 (or device 105, 107, or 109) as described herein may be spread across multiple data processing devices, for example, to distribute processing load across multiple computers, to segregate transactions based on geographic location, user access level, quality of service (QoS), etc.

One or more aspects may be embodied in computer-usable or readable data and/or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices as described herein. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The modules may be written in a source code programming language that is subsequently compiled for execution, or may be written in a scripting language such as (but not limited to) HyperText Markup Language (HTML) or Extensible Markup Language (XML). The computer executable instructions may be stored on a computer readable medium such as a nonvolatile storage device. Any suitable computer readable storage media may be utilized, including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, solid state storage devices, and/or any combination thereof. In addition, various transmission (non-storage) media representing data or events as described herein may be transferred between a source and a destination in the form of electromagnetic waves traveling through signal-conducting media such as metal wires, optical fibers, and/or wireless transmission media (e.g., air and/or space). Various aspects described herein may be embodied as a method, a data processing system, or a computer program product. Therefore, various functionalities may be embodied in whole or in part in software, firmware, and/or hardware or hardware equivalents such as integrated circuits, field programmable gate arrays (FPGA), and the like. Particular data structures may be used to more effectively implement one or more aspects described herein, and such data structures are contemplated within the scope of computer executable instructions and computer-usable data described herein.

With further reference to FIG. 2, one or more aspects described herein may be implemented in a remote-access environment. FIG. 2 depicts an example system architecture including a computing device 201 in an illustrative computing environment 200 that may be used according to one or more illustrative aspects described herein. Computing device 201 may be used as a server 206 a in a single-server or multi-server desktop virtualization system (e.g., a remote access or cloud system) and can be configured to provide virtual machines for client access devices. The computing device 201 may have a processor 203 for controlling overall operation of the device 201 and its associated components, including RAM 205, ROM 207, Input/Output (I/O) module 209, and memory 215.

I/O module 209 may include a mouse, keypad, touch screen, scanner, optical reader, and/or stylus (or other input device(s)) through which a user of computing device 201 may provide input, and may also include one or more of a speaker for providing audio output and one or more of a video display device for providing textual, audiovisual, and/or graphical output. Software may be stored within memory 215 and/or other storage to provide instructions to processor 203 for configuring computing device 201 into a special purpose computing device in order to perform various functions as described herein. For example, memory 215 may store software used by the computing device 201, such as an operating system 217, application programs 219, and an associated database 221.

Computing device 201 may operate in a networked environment supporting connections to one or more remote computers, such as terminals 240 (also referred to as client devices and/or client machines). The terminals 240 may be personal computers, mobile devices, laptop computers, tablets, or servers that include many or all of the elements described above with respect to the computing device 103 or 201. The network connections depicted in FIG. 2 include a local area network (LAN) 225 and a wide area network (WAN) 229, but may also include other networks. When used in a LAN networking environment, computing device 201 may be connected to the LAN 225 through a network interface or adapter 223. When used in a WAN networking environment, computing device 201 may include a modem or other wide area network interface 227 for establishing communications over the WAN 229, such as computer network 230 (e.g., the Internet). It will be appreciated that the network connections shown are illustrative and other means of establishing a communications link between the computers may be used. Computing device 201 and/or terminals 240 may also be mobile terminals (e.g., mobile phones, smartphones, personal digital assistants (PDAs), notebooks, etc.) including various other components, such as a battery, speaker, and antennas (not shown).

Aspects described herein may also be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of other computing systems, environments, and/or configurations that may be suitable for use with aspects described herein include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network personal computers (PCs), minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

As shown in FIG. 2, one or more client devices 240 may be in communication with one or more servers 206 a-206 n (generally referred to herein as “server(s) 206”). In one embodiment, the computing environment 200 may include a network appliance installed between the server(s) 206 and client machine(s) 240. The network appliance may manage client/server connections, and in some cases can load balance client connections amongst a plurality of backend servers 206.

The client machine(s) 240 may in some embodiments be referred to as a single client machine 240 or a single group of client machines 240, while server(s) 206 may be referred to as a single server 206 or a single group of servers 206. In one embodiment a single client machine 240 communicates with more than one server 206, while in another embodiment a single server 206 communicates with more than one client machine 240. In yet another embodiment, a single client machine 240 communicates with a single server 206.

A client machine 240 can, in some embodiments, be referenced by any one of the following non-exhaustive terms: client machine(s); client(s); client computer(s); client device(s); client computing device(s); local machine; remote machine; client node(s); endpoint(s); or endpoint node(s). The server 206, in some embodiments, may be referenced by any one of the following non-exhaustive terms: server(s), local machine; remote machine; server farm(s), or host computing device(s).

In one embodiment, the client machine 240 may be a virtual machine. The virtual machine may be any virtual machine, while in some embodiments the virtual machine may be any virtual machine managed by a Type 1 or Type 2 hypervisor, for example, a hypervisor developed by Citrix Systems, IBM, VMware, or any other hypervisor. In some aspects, the virtual machine may be managed by a hypervisor, while in other aspects the virtual machine may be managed by a hypervisor executing on a server 206 or a hypervisor executing on a client 240.

Some embodiments include a client device 240 that displays application output generated by an application remotely executing on a server 206 or other remotely located machine. In these embodiments, the client device 240 may execute a virtual machine receiver program or application to display the output in an application window, a browser, or other output window. In one example, the application is a desktop, while in other examples the application is an application that generates or presents a desktop. A desktop may include a graphical shell providing a user interface for an instance of an operating system in which local and/or remote applications can be integrated. Applications, as used herein, are programs that execute after an instance of an operating system (and, optionally, also the desktop) has been loaded.

The server 206, in some embodiments, uses a remote presentation protocol or other program to send data to a thin-client or remote-display application executing on the client to present display output generated by an application executing on the server 206. The thin-client or remote-display protocol can be any one of the following non-exhaustive list of protocols: the Independent Computing Architecture (ICA) protocol developed by Citrix Systems, Inc. of Ft. Lauderdale, Fla.; or the Remote Desktop Protocol (RDP) manufactured by the Microsoft Corporation of Redmond, Wash.

A remote computing environment may include more than one server 206 a-206 n such that the servers 206 a-206 n are logically grouped together into a server farm 206, for example, in a cloud computing environment. The server farm 206 may include servers 206 that are geographically dispersed while logically grouped together, or servers 206 that are located proximate to each other while logically grouped together. Geographically dispersed servers 206 a-206 n within a server farm 206 can, in some embodiments, communicate using a WAN (wide), MAN (metropolitan), or LAN (local), where different geographic regions can be characterized as: different continents; different regions of a continent; different countries; different states; different cities; different campuses; different rooms; or any combination of the preceding geographical locations. In some embodiments the server farm 206 may be administered as a single entity, while in other embodiments the server farm 206 can include multiple server farms.

In some embodiments, a server farm may include servers 206 that execute a substantially similar type of operating system platform (e.g., WINDOWS, UNIX, LINUX, iOS, ANDROID, etc.) In other embodiments, server farm 206 may include a first group of one or more servers that execute a first type of operating system platform, and a second group of one or more servers that execute a second type of operating system platform.

Server 206 may be configured as any type of server, as needed, e.g., a file server, an application server, a web server, a proxy server, an appliance, a network appliance, a gateway, an application gateway, a gateway server, a virtualization server, a deployment server, a Secure Sockets Layer (SSL) VPN server, a firewall, a web server, an application server or as a master application server, a server executing an active directory, or a server executing an application acceleration program that provides firewall functionality, application functionality, or load balancing functionality. Other server types may also be used.

Some embodiments include a first server 206 a that receives requests from a client machine 240, forwards the request to a second server 206 b (not shown), and responds to the request generated by the client machine 240 with a response from the second server 206 b (not shown.) First server 206 a may acquire an enumeration of applications available to the client machine 240 as well as address information associated with an application server 206 hosting an application identified within the enumeration of applications. First server 206 a can then present a response to the client's request using a web interface, and communicate directly with the client 240 to provide the client 240 with access to an identified application. One or more clients 240 and/or one or more servers 206 may transmit data over network 230, e.g., network 101.

FIG. 3 shows a high-level architecture of an illustrative desktop virtualization system. As shown, the desktop virtualization system may be single-server or multi-server system, or cloud system, including at least one virtualization server 301 configured to provide virtual desktops and/or virtual applications to one or more client access devices 240. As used herein, a desktop refers to a graphical environment or space in which one or more applications may be hosted and/or executed. A desktop may include a graphical shell providing a user interface for an instance of an operating system in which local and/or remote applications can be integrated. Applications may include programs that execute after an instance of an operating system (and, optionally, also the desktop) has been loaded. Each instance of the operating system may be physical (e.g., one operating system per device) or virtual (e g, many instances of an OS running on a single device). Each application may be executed on a local device, or executed on a remotely located device (e.g., remoted).

A computer device 301 may be configured as a virtualization server in a virtualization environment, for example, a single-server, multi-server, or cloud computing environment. Virtualization server 301 illustrated in FIG. 3 can be deployed as and/or implemented by one or more embodiments of the server 206 illustrated in FIG. 2 or by other known computing devices. Included in virtualization server 301 is a hardware layer that can include one or more physical disks 304, one or more physical devices 306, one or more physical processors 308, and one or more physical memories 316. In some embodiments, firmware 312 can be stored within a memory element in the physical memory 316 and can be executed by one or more of the physical processors 308. Virtualization server 301 may further include an operating system 314 that may be stored in a memory element in the physical memory 316 and executed by one or more of the physical processors 308. Still further, a hypervisor 302 may be stored in a memory element in the physical memory 316 and can be executed by one or more of the physical processors 308.

Executing on one or more of the physical processors 308 may be one or more virtual machines 332A-C (generally 332). Each virtual machine 332 may have a virtual disk 326A-C and a virtual processor 328A-C. In some embodiments, a first virtual machine 332A may execute, using a virtual processor 328A, a control program 320 that includes a tools stack 324. Control program 320 may be referred to as a control virtual machine, Dom0, Domain 0, or other virtual machine used for system administration and/or control. In some embodiments, one or more virtual machines 332B-C can execute, using a virtual processor 328B-C, a guest operating system 330A-B.

Virtualization server 301 may include a hardware layer 310 with one or more pieces of hardware that communicate with the virtualization server 301. In some embodiments, the hardware layer 310 can include one or more physical disks 304, one or more physical devices 306, one or more physical processors 308, and one or more physical memory 316. Physical components 304, 306, 308, and 316 may include, for example, any of the components described above. Physical devices 306 may include, for example, a network interface card, a video card, a keyboard, a mouse, an input device, a monitor, a display device, speakers, an optical drive, a storage device, a universal serial bus connection, a printer, a scanner, a network element (e.g., router, firewall, network address translator, load balancer, virtual private network (VPN) gateway, Dynamic Host Configuration Protocol (DHCP) router, etc.), or any device connected to or communicating with virtualization server 301. Physical memory 316 in the hardware layer 310 may include any type of memory. Physical memory 316 may store data, and in some embodiments may store one or more programs, or set of executable instructions. FIG. 3 illustrates an embodiment where firmware 312 is stored within the physical memory 316 of virtualization server 301. Programs or executable instructions stored in the physical memory 316 can be executed by the one or more processors 308 of virtualization server 301.

Virtualization server 301 may also include a hypervisor 302. In some embodiments, hypervisor 302 may be a program executed by processors 308 on virtualization server 301 to create and manage any number of virtual machines 332. Hypervisor 302 may be referred to as a virtual machine monitor, or platform virtualization software. In some embodiments, hypervisor 302 can be any combination of executable instructions and hardware that monitors virtual machines executing on a computing machine. Hypervisor 302 may be Type 2 hypervisor, where the hypervisor executes within an operating system 314 executing on the virtualization server 301. Virtual machines may then execute at a level above the hypervisor 302. In some embodiments, the Type 2 hypervisor may execute within the context of a user's operating system such that the Type 2 hypervisor interacts with the user's operating system. In other embodiments, one or more virtualization servers 301 in a virtualization environment may instead include a Type 1 hypervisor (not shown). A Type 1 hypervisor may execute on the virtualization server 301 by directly accessing the hardware and resources within the hardware layer 310. That is, while a Type 2 hypervisor 302 accesses system resources through a host operating system 314, as shown, a Type 1 hypervisor may directly access all system resources without the host operating system 314. A Type 1 hypervisor may execute directly on one or more physical processors 308 of virtualization server 301, and may include program data stored in the physical memory 316.

Hypervisor 302, in some embodiments, can provide virtual resources to operating systems 330 or control programs 320 executing on virtual machines 332 in any manner that simulates the operating systems 330 or control programs 320 having direct access to system resources. System resources can include, but are not limited to, physical devices 306, physical disks 304, physical processors 308, physical memory 316, and any other component included in hardware layer 310 of the virtualization server 301. Hypervisor 302 may be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and/or execute virtual machines that provide access to computing environments. In still other embodiments, hypervisor 302 may control processor scheduling and memory partitioning for a virtual machine 332 executing on virtualization server 301. Hypervisor 302 may include those manufactured by VMWare, Inc., of Palo Alto, Calif.; HyperV, VirtualServer or virtual PC hypervisors provided by Microsoft, or others. In some embodiments, virtualization server 301 may execute a hypervisor 302 that creates a virtual machine platform on which guest operating systems may execute. In these embodiments, the virtualization server 301 may be referred to as a host server. An example of such a virtualization server is the Citrix Hypervisor provided by Citrix Systems, Inc., of Fort Lauderdale, Fla.

Hypervisor 302 may create one or more virtual machines 332B-C (generally 332) in which guest operating systems 330 execute. In some embodiments, hypervisor 302 may load a virtual machine image to create a virtual machine 332. In other embodiments, the hypervisor 302 may execute a guest operating system 330 within virtual machine 332. In still other embodiments, virtual machine 332 may execute guest operating system 330.

In addition to creating virtual machines 332, hypervisor 302 may control the execution of at least one virtual machine 332. In other embodiments, hypervisor 302 may present at least one virtual machine 332 with an abstraction of at least one hardware resource provided by the virtualization server 301 (e.g., any hardware resource available within the hardware layer 310). In other embodiments, hypervisor 302 may control the manner in which virtual machines 332 access physical processors 308 available in virtualization server 301. Controlling access to physical processors 308 may include determining whether a virtual machine 332 should have access to a processor 308, and how physical processor capabilities are presented to the virtual machine 332.

As shown in FIG. 3, virtualization server 301 may host or execute one or more virtual machines 332. A virtual machine 332 is a set of executable instructions that, when executed by a processor 308, may imitate the operation of a physical computer such that the virtual machine 332 can execute programs and processes much like a physical computing device. While FIG. 3 illustrates an embodiment where a virtualization server 301 hosts three virtual machines 332, in other embodiments virtualization server 301 can host any number of virtual machines 332. Hypervisor 302, in some embodiments, may provide each virtual machine 332 with a unique virtual view of the physical hardware, memory, processor, and other system resources available to that virtual machine 332. In some embodiments, the unique virtual view can be based on one or more of virtual machine permissions, application of a policy engine to one or more virtual machine identifiers, a user accessing a virtual machine, the applications executing on a virtual machine, networks accessed by a virtual machine, or any other desired criteria. For instance, hypervisor 302 may create one or more unsecure virtual machines 332 and one or more secure virtual machines 332. Unsecure virtual machines 332 may be prevented from accessing resources, hardware, memory locations, and programs that secure virtual machines 332 may be permitted to access. In other embodiments, hypervisor 302 may provide each virtual machine 332 with a substantially similar virtual view of the physical hardware, memory, processor, and other system resources available to the virtual machines 332.

Each virtual machine 332 may include a virtual disk 326A-C (generally 326) and a virtual processor 328A-C (generally 328.) The virtual disk 326, in some embodiments, is a virtualized view of one or more physical disks 304 of the virtualization server 301, or a portion of one or more physical disks 304 of the virtualization server 301. The virtualized view of the physical disks 304 can be generated, provided, and managed by the hypervisor 302. In some embodiments, hypervisor 302 provides each virtual machine 332 with a unique view of the physical disks 304. Thus, in these embodiments, the particular virtual disk 326 included in each virtual machine 332 can be unique when compared with the other virtual disks 326.

A virtual processor 328 can be a virtualized view of one or more physical processors 308 of the virtualization server 301. In some embodiments, the virtualized view of the physical processors 308 can be generated, provided, and managed by hypervisor 302. In some embodiments, virtual processor 328 has substantially all of the same characteristics of at least one physical processor 308. In other embodiments, virtual processor 308 provides a modified view of physical processors 308 such that at least some of the characteristics of the virtual processor 328 are different than the characteristics of the corresponding physical processor 308.

With further reference to FIG. 4, some aspects described herein may be implemented in a cloud-based environment. FIG. 4 illustrates an example of a cloud computing environment (or cloud system) 400. As seen in FIG. 4, client computers 411-414 may communicate with a cloud management server 410 to access the computing resources (e.g., host servers 403 a-403 b (generally referred herein as “host servers 403”), storage resources 404 a-404 b (generally referred herein as “storage resources 404”), and network elements 405 a-405 b (generally referred herein as “network resources 405”)) of the cloud system.

Management server 410 may be implemented on one or more physical servers. The management server 410 may run, for example, Citrix Cloud by Citrix Systems, Inc. of Ft. Lauderdale, Fla., or OPENSTACK, among others. Management server 410 may manage various computing resources, including cloud hardware and software resources, for example, host computers 403, data storage devices 404, and networking devices 405. The cloud hardware and software resources may include private and/or public components. For example, a cloud may be configured as a private cloud to be used by one or more particular customers or client computers 411-414 and/or over a private network. In other embodiments, public clouds or hybrid public-private clouds may be used by other customers over an open or hybrid networks.

Management server 410 may be configured to provide user interfaces through which cloud operators and cloud customers may interact with the cloud system 400. For example, the management server 410 may provide a set of application programming interfaces (APIs) and/or one or more cloud operator console applications (e.g., web-based or standalone applications) with user interfaces to allow cloud operators to manage the cloud resources, configure the virtualization layer, manage customer accounts, and perform other cloud administration tasks. The management server 410 also may include a set of APIs and/or one or more customer console applications with user interfaces configured to receive cloud computing requests from end users via client computers 411-414, for example, requests to create, modify, or destroy virtual machines within the cloud. Client computers 411-414 may connect to management server 410 via the Internet or some other communication network, and may request access to one or more of the computing resources managed by management server 410. In response to client requests, the management server 410 may include a resource manager configured to select and provision physical resources in the hardware layer of the cloud system based on the client requests. For example, the management server 410 and additional components of the cloud system may be configured to provision, create, and manage virtual machines and their operating environments (e.g., hypervisors, storage resources, services offered by the network elements, etc.) for customers at client computers 411-414, over a network (e.g., the Internet), providing customers with computational resources, data storage services, networking capabilities, and computer platform and application support. Cloud systems also may be configured to provide various specific services, including security systems, development environments, user interfaces, and the like.

Certain clients 411-414 may be related, for example, to different client computers creating virtual machines on behalf of the same end user, or different users affiliated with the same company or organization. In other examples, certain clients 411-414 may be unrelated, such as users affiliated with different companies or organizations. For unrelated clients, information on the virtual machines or storage of any one user may be hidden from other users.

Referring now to the physical hardware layer of a cloud computing environment, availability zones 401-402 (or zones) may refer to a collocated set of physical computing resources. Zones may be geographically separated from other zones in the overall cloud of computing resources. For example, zone 401 may be a first cloud datacenter located in California, and zone 402 may be a second cloud datacenter located in Florida. Management server 410 may be located at one of the availability zones, or at a separate location. Each zone may include an internal network that interfaces with devices that are outside of the zone, such as the management server 410, through a gateway. End users of the cloud (e.g., clients 411-414) might or might not be aware of the distinctions between zones. For example, an end user may request the creation of a virtual machine having a specified amount of memory, processing power, and network capabilities. The management server 410 may respond to the user's request and may allocate the resources to create the virtual machine without the user knowing whether the virtual machine was created using resources from zone 401 or zone 402. In other examples, the cloud system may allow end users to request that virtual machines (or other cloud resources) are allocated in a specific zone or on specific resources 403-405 within a zone.

In this example, each zone 401-402 may include an arrangement of various physical hardware components (or computing resources) 403-405, for example, physical hosting resources (or processing resources), physical network resources, physical storage resources, switches, and additional hardware resources that may be used to provide cloud computing services to customers. The physical hosting resources in a cloud zone 401-402 may include one or more computer servers 403, such as the virtualization servers 301 described above, which may be configured to create and host virtual machine instances. The physical network resources in a cloud zone 401 or 402 may include one or more network elements 405 (e.g., network service providers) comprising hardware and/or software configured to provide a network service to cloud customers, such as firewalls, network address translators, load balancers, virtual private network (VPN) gateways, Dynamic Host Configuration Protocol (DHCP) routers, and the like. The storage resources in the cloud zone 401-402 may include storage disks (e.g., solid state drives (SSDs), magnetic hard disks, etc.) and other storage devices.

The example cloud computing environment shown in FIG. 4 also may include a virtualization layer (e.g., as shown in FIGS. 1-3) with additional hardware and/or software resources configured to create and manage virtual machines and provide other services to customers using the physical resources in the cloud. The virtualization layer may include hypervisors, as described above in FIG. 3, along with other components to provide network virtualizations, storage virtualizations, etc. The virtualization layer may be as a separate layer from the physical resource layer, or may share some or all of the same hardware and/or software resources with the physical resource layer. For example, the virtualization layer may include a hypervisor installed in each of the virtualization servers 403 with the physical computing resources. Known cloud systems may alternatively be used, e.g., WINDOWS AZURE (Microsoft Corporation of Redmond Wash.), AMAZON EC2 (Amazon.com Inc. of Seattle, Wash.), IBM BLUE CLOUD (IBM Corporation of Armonk, N.Y.), or others.

Enterprise Mobility Management Architecture

FIG. 5 represents an enterprise mobility technical architecture 500 for use in a “Bring Your Own Device” (BYOD) environment. The architecture enables a user of a mobile device 502 to both access enterprise or personal resources from a mobile device 502 and use the mobile device 502 for personal use. The user may access such enterprise resources 504 or enterprise services 508 using a mobile device 502 that is purchased by the user or a mobile device 502 that is provided by the enterprise to the user. The user may utilize the mobile device 502 for business use only or for business and personal use. The mobile device 502 may run an iOS operating system, an Android operating system, or the like. The enterprise may choose to implement policies to manage the mobile device 502. The policies may be implemented through a firewall or gateway in such a way that the mobile device 502 may be identified, secured or security verified, and provided selective or full access to the enterprise resources (e.g., 504 and 508.) The policies may be mobile device management policies, mobile application management policies, mobile data management policies, or some combination of mobile device, application, and data management policies. A mobile device 502 that is managed through the application of mobile device management policies may be referred to as an enrolled device.

In some embodiments, the operating system of the mobile device 502 may be separated into a managed partition 510 and an unmanaged partition 512. The managed partition 510 may have policies applied to it to secure the applications running on and data stored in the managed partition 510. The applications running on the managed partition 510 may be secure applications. In other embodiments, all applications may execute in accordance with a set of one or more policy files received separate from the application, and which define one or more security parameters, features, resource restrictions, and/or other access controls that are enforced by the mobile device management system when that application is executing on the mobile device 502. By operating in accordance with their respective policy file(s), each application may be allowed or restricted from communications with one or more other applications and/or resources, thereby creating a virtual partition. Thus, as used herein, a partition may refer to a physically partitioned portion of memory (physical partition), a logically partitioned portion of memory (logical partition), and/or a virtual partition created as a result of enforcement of one or more policies and/or policy files across multiple applications as described herein (virtual partition). Stated differently, by enforcing policies on managed applications, those applications may be restricted to only be able to communicate with other managed applications and trusted enterprise resources, thereby creating a virtual partition that is not accessible by unmanaged applications and devices.

The secure applications may be email applications, web browsing applications, software-as-a-service (SaaS) access applications, Windows Application access applications, and the like. The secure applications may be secure native applications 514, secure remote applications 522 executed by a secure application launcher 518, virtualization applications 526 executed by a secure application launcher 518, and the like. The secure native applications 514 may be wrapped by a secure application wrapper 520. The secure application wrapper 520 may include integrated policies that are executed on the mobile device 502 when the secure native application 514 is executed on the mobile device 502. The secure application wrapper 520 may include meta-data that points the secure native application 514 running on the mobile device 502 to the resources hosted at the enterprise (e.g., 504 and 508) that the secure native application 514 may require to complete the task requested upon execution of the secure native application 514. The secure remote applications 522 executed by a secure application launcher 518 may be executed within the secure application launcher 518. The virtualization applications 526 executed by a secure application launcher 518 may utilize resources on the mobile device 502, at the enterprise resources 504, and the like. The resources used on the mobile device 502 by the virtualization applications 526 executed by a secure application launcher 518 may include user interaction resources, processing resources, and the like. The user interaction resources may be used to collect and transmit keyboard input, mouse input, camera input, tactile input, audio input, visual input, gesture input, and the like. The processing resources may be used to present a user interface, process data received from the enterprise resources 504, and the like. The resources used at the enterprise resources 504 by the virtualization applications 526 executed by a secure application launcher 518 may include user interface generation resources, processing resources, and the like. The user interface generation resources may be used to assemble a user interface, modify a user interface, refresh a user interface, and the like. The processing resources may be used to create information, read information, update information, delete information, and the like. For example, the virtualization application 526 may record user interactions associated with a graphical user interface (GUI) and communicate them to a server application where the server application will use the user interaction data as an input to the application operating on the server. In such an arrangement, an enterprise may elect to maintain the application on the server side as well as data, files, etc. associated with the application. While an enterprise may elect to “mobilize” some applications in accordance with the principles herein by securing them for deployment on the mobile device 502, this arrangement may also be elected for certain applications. For example, while some applications may be secured for use on the mobile device 502, others might not be prepared or appropriate for deployment on the mobile device 502 so the enterprise may elect to provide the mobile user access to the unprepared applications through virtualization techniques. As another example, the enterprise may have large complex applications with large and complex data sets (e.g., material resource planning applications) where it would be very difficult, or otherwise undesirable, to customize the application for the mobile device 502 so the enterprise may elect to provide access to the application through virtualization techniques. As yet another example, the enterprise may have an application that maintains highly secured data (e.g., human resources data, customer data, engineering data) that may be deemed by the enterprise as too sensitive for even the secured mobile environment so the enterprise may elect to use virtualization techniques to permit mobile access to such applications and data. An enterprise may elect to provide both fully secured and fully functional applications on the mobile device 502 as well as a virtualization application 526 to allow access to applications that are deemed more properly operated on the server side. In an embodiment, the virtualization application 526 may store some data, files, etc. on the mobile device 502 in one of the secure storage locations. An enterprise, for example, may elect to allow certain information to be stored on the mobile device 502 while not permitting other information.

In connection with the virtualization application 526, as described herein, the mobile device 502 may have a virtualization application 526 that is designed to present GUIs and then record user interactions with the GUI. The virtualization application 526 may communicate the user interactions to the server side to be used by the server side application as user interactions with the application. In response, the application on the server side may transmit back to the mobile device 502 a new GUI. For example, the new GUI may be a static page, a dynamic page, an animation, or the like, thereby providing access to remotely located resources.

The secure applications 514 may access data stored in a secure data container 528 in the managed partition 510 of the mobile device 502. The data secured in the secure data container may be accessed by the secure native applications 514, secure remote applications 522 executed by a secure application launcher 518, virtualization applications 526 executed by a secure application launcher 518, and the like. The data stored in the secure data container 528 may include files, databases, and the like. The data stored in the secure data container 528 may include data restricted to a specific secure application 530, shared among secure applications 532, and the like. Data restricted to a secure application may include secure general data 534 and highly secure data 538. Secure general data may use a strong form of encryption such as Advanced Encryption Standard (AES) 128-bit encryption or the like, while highly secure data 538 may use a very strong form of encryption such as AES 256-bit encryption. Data stored in the secure data container 528 may be deleted from the mobile device 502 upon receipt of a command from the device manager 524. The secure applications (e.g., 514, 522, and 526) may have a dual-mode option 540. The dual mode option 540 may present the user with an option to operate the secured application in an unsecured or unmanaged mode. In an unsecured or unmanaged mode, the secure applications may access data stored in an unsecured data container 542 on the unmanaged partition 512 of the mobile device 502. The data stored in an unsecured data container may be personal data 544. The data stored in an unsecured data container 542 may also be accessed by unsecured applications 546 that are running on the unmanaged partition 512 of the mobile device 502. The data stored in an unsecured data container 542 may remain on the mobile device 502 when the data stored in the secure data container 528 is deleted from the mobile device 502. An enterprise may want to delete from the mobile device 502 selected or all data, files, and/or applications owned, licensed or controlled by the enterprise (enterprise data) while leaving or otherwise preserving personal data, files, and/or applications owned, licensed or controlled by the user (personal data). This operation may be referred to as a selective wipe. With the enterprise and personal data arranged in accordance to the aspects described herein, an enterprise may perform a selective wipe.

The mobile device 502 may connect to enterprise resources 504 and enterprise services 508 at an enterprise, to the public Internet 548, and the like. The mobile device 502 may connect to enterprise resources 504 and enterprise services 508 through virtual private network connections. The virtual private network connections, also referred to as microVPN or application-specific VPN, may be specific to particular applications (as illustrated by microVPNs 550, particular devices, particular secured areas on the mobile device (as illustrated by O/S VPN 552), and the like. For example, each of the wrapped applications in the secured area of the mobile device 502 may access enterprise resources through an application specific VPN such that access to the VPN would be granted based on attributes associated with the application, possibly in conjunction with user or device attribute information. The virtual private network connections may carry Microsoft Exchange traffic, Microsoft Active Directory traffic, HyperText Transfer Protocol (HTTP) traffic, HyperText Transfer Protocol Secure (HTTPS) traffic, application management traffic, and the like. The virtual private network connections may support and enable single-sign-on authentication processes 554. The single-sign-on processes may allow a user to provide a single set of authentication credentials, which are then verified by an authentication service 558. The authentication service 558 may then grant to the user access to multiple enterprise resources 504, without requiring the user to provide authentication credentials to each individual enterprise resource 504.

The virtual private network connections may be established and managed by an access gateway 560. The access gateway 560 may include performance enhancement features that manage, accelerate, and improve the delivery of enterprise resources 504 to the mobile device 502. The access gateway 560 may also re-route traffic from the mobile device 502 to the public Internet 548, enabling the mobile device 502 to access publicly available and unsecured applications that run on the public Internet 548. The mobile device 502 may connect to the access gateway via a transport network 562. The transport network 562 may use one or more transport protocols and may be a wired network, wireless network, cloud network, local area network, metropolitan area network, wide area network, public network, private network, and the like.

The enterprise resources 504 may include email servers, file sharing servers, SaaS applications, Web application servers, Windows application servers, and the like. Email servers may include Exchange servers, Lotus Notes servers, and the like. File sharing servers may include ShareFile servers, and the like. SaaS applications may include Salesforce, and the like. Windows application servers may include any application server that is built to provide applications that are intended to run on a local Windows operating system, and the like. The enterprise resources 504 may be premise-based resources, cloud-based resources, and the like. The enterprise resources 504 may be accessed by the mobile device 502 directly or through the access gateway 560. The enterprise resources 504 may be accessed by the mobile device 502 via the transport network 562.

The enterprise services 508 may include authentication services 558, threat detection services 564, device manager services 524, file sharing services 568, policy manager services 570, social integration services 572, application controller services 574, and the like. Authentication services 558 may include user authentication services, device authentication services, application authentication services, data authentication services, and the like. Authentication services 558 may use certificates. The certificates may be stored on the mobile device 502, by the enterprise resources 504, and the like. The certificates stored on the mobile device 502 may be stored in an encrypted location on the mobile device 502, the certificate may be temporarily stored on the mobile device 502 for use at the time of authentication, and the like. Threat detection services 564 may include intrusion detection services, unauthorized access attempt detection services, and the like. Unauthorized access attempt detection services may include unauthorized attempts to access devices, applications, data, and the like. Device management services 524 may include configuration, provisioning, security, support, monitoring, reporting, and decommissioning services. File sharing services 568 may include file management services, file storage services, file collaboration services, and the like. Policy manager services 570 may include device policy manager services, application policy manager services, data policy manager services, and the like. Social integration services 572 may include contact integration services, collaboration services, integration with social networks such as Facebook, Twitter, and LinkedIn, and the like. Application controller services 574 may include management services, provisioning services, deployment services, assignment services, revocation services, wrapping services, and the like.

The enterprise mobility technical architecture 500 may include an application store 578. The application store 578 may include unwrapped applications 580, pre-wrapped applications 582, and the like. Applications may be populated in the application store 578 from the application controller 574. The application store 578 may be accessed by the mobile device 502 through the access gateway 560, through the public Internet 548, or the like. The application store 578 may be provided with an intuitive and easy to use user interface.

A software development kit 584 may provide a user the capability to secure applications selected by the user by wrapping the application as described previously in this description. An application that has been wrapped using the software development kit 584 may then be made available to the mobile device 502 by populating it in the application store 578 using the application controller 574.

The enterprise mobility technical architecture 500 may include a management and analytics capability 588. The management and analytics capability 588 may provide information related to how resources are used, how often resources are used, and the like. Resources may include devices, applications, data, and the like. How resources are used may include which devices download which applications, which applications access which data, and the like. How often resources are used may include how often an application has been downloaded, how many times a specific set of data has been accessed by an application, and the like.

FIG. 6 is another illustrative enterprise mobility management system 600. Some of the components of the mobility management system 500 described above with reference to FIG. 5 have been omitted for the sake of simplicity. The architecture of the system 600 depicted in FIG. 6 is similar in many respects to the architecture of the system 500 described above with reference to FIG. 5 and may include additional features not mentioned above.

In this case, the left hand side represents an enrolled mobile device 602 with a client agent 604, which interacts with gateway server 606 (which includes Access Gateway and application controller functionality) to access various enterprise resources 608 and services 609 such as Exchange, Sharepoint, public-key infrastructure (PKI) Resources, Kerberos Resources, Certificate Issuance service, as shown on the right hand side above. Although not specifically shown, the mobile device 602 may also interact with an enterprise application store (StoreFront) for the selection and downloading of applications.

The client agent 604 acts as the UI (user interface) intermediary for Windows apps/desktops hosted in an Enterprise data center, which are accessed using the High-Definition User Experience (HDX)/ICA display remoting protocol. The client agent 604 also supports the installation and management of native applications on the mobile device 602, such as native iOS or Android applications. For example, the managed applications 610 (mail, browser, wrapped application) shown in the figure above are all native applications that execute locally on the mobile device 602. Client agent 604 and application management framework of this architecture act to provide policy driven management capabilities and features such as connectivity and SSO (single sign on) to enterprise resources/services 608. The client agent 604 handles primary user authentication to the enterprise, normally to Access Gateway (AG) 606 with SSO to other gateway server components. The client agent 604 obtains policies from gateway server 606 to control the behavior of the managed applications 610 on the mobile device 602.

The Secure InterProcess Communication (IPC) links 612 between the native applications 610 and client agent 604 represent a management channel, which may allow a client agent to supply policies to be enforced by the application management framework 614 “wrapping” each application. The IPC channel 612 may also allow client agent 604 to supply credential and authentication information that enables connectivity and SSO to enterprise resources 608. Finally, the IPC channel 612 may allow the application management framework 614 to invoke user interface functions implemented by client agent 604, such as online and offline authentication.

Communications between the client agent 604 and gateway server 606 are essentially an extension of the management channel from the application management framework 614 wrapping each native managed application 610. The application management framework 614 may request policy information from client agent 604, which in turn may request it from gateway server 606. The application management framework 614 may request authentication, and client agent 604 may log into the gateway services part of gateway server 606 (for example, Citrix Gateway). Client agent 604 may also call supporting services on gateway server 606, which may produce input material to derive encryption keys for the local data vaults 616, or may provide client certificates which may enable direct authentication to PKI protected resources, as more fully explained below.

In more detail, the application management framework 614 “wraps” each managed application 610. This may be incorporated via an explicit build step, or via a post-build processing step. The application management framework 614 may “pair” with client agent 604 on first launch of an application 610 to initialize the Secure IPC channel 612 and obtain the policy for that application. The application management framework 614 may enforce relevant portions of the policy that apply locally, such as the client agent login dependencies and some of the containment policies that restrict how local OS services may be used, or how they may interact with the managed application 610.

The application management framework 614 may use services provided by client agent 604 over the Secure IPC channel 612 to facilitate authentication and internal network access. Key management for the private and shared data vaults 616 (containers) may be also managed by appropriate interactions between the managed applications 610 and client agent 604. Vaults 616 may be available only after online authentication, or may be made available after offline authentication if allowed by policy. First use of vaults 616 may require online authentication, and offline access may be limited to at most the policy refresh period before online authentication is again required.

Network access to internal resources may occur directly from individual managed applications 610 through Access Gateway 606. The application management framework 614 may be responsible for orchestrating the network access on behalf of each managed application 610. Client agent 604 may facilitate these network connections by providing suitable time limited secondary credentials obtained following online authentication. Multiple modes of network connection may be used, such as reverse web proxy connections and end-to-end VPN-style tunnels 618.

The Mail and Browser managed applications 610 have special status and may make use of facilities that might not be generally available to arbitrary wrapped applications. For example, the Mail application 610 may use a special background network access mechanism that allows it to access an Exchange server 608 over an extended period of time without requiring a full AG logon. The Browser application 610 may use multiple private data vaults 616 to segregate different kinds of data.

This architecture may support the incorporation of various other security features. For example, gateway server 606 (including its gateway services) in some cases may not need to validate active directory (AD) passwords. It can be left to the discretion of an enterprise whether an AD password may be used as an authentication factor for some users in some situations. Different authentication methods may be used if a user is online or offline (i.e., connected or not connected to a network).

Step up authentication is a feature wherein gateway server 606 may identify managed native applications 610 that are allowed to have access to highly classified data requiring strong authentication, and ensure that access to these applications is only permitted after performing appropriate authentication, even if this means a re-authentication is required by the user after a prior weaker level of login.

Another security feature of this solution is the encryption of the data vaults 616 (containers) on the mobile device 602. The vaults 616 may be encrypted so that all on-device data including files, databases, and configurations are protected. For on-line vaults, the keys may be stored on the server (gateway server 606), and for off-line vaults, a local copy of the keys may be protected by a user password or biometric validation. If or when data is stored locally on the mobile device 602 in the secure container 616, it may be preferred that a minimum of AES 256 encryption algorithm be utilized.

Other secure container features may also be implemented. For example, a logging feature may be included, wherein security events happening inside a managed application 610 may be logged and reported to the backend. Data wiping may be supported, such as if or when the managed application 610 detects tampering, associated encryption keys may be written over with random data, leaving no hint on the file system that user data was destroyed. Screenshot protection may be another feature, where an application may prevent any data from being stored in screenshots. For example, the key window's hidden property may be set to YES. This may cause whatever content is currently displayed on the screen to be hidden, resulting in a blank screenshot where any content would normally reside.

Local data transfer may be prevented, such as by preventing any data from being locally transferred outside the application container, e.g., by copying it or sending it to an external application. A keyboard cache feature may operate to disable the autocorrect functionality for sensitive text fields. SSL certificate validation may be operable so the application specifically validates the server SSL certificate instead of it being stored in the keychain. An encryption key generation feature may be used such that the key used to encrypt data on the mobile device 602 is generated using a passphrase or biometric data supplied by the user (if offline access is required). It may be XORed with another key randomly generated and stored on the server side if offline access is not required. Key Derivation functions may operate such that keys generated from the user password use KDFs (key derivation functions, notably Password-Based Key Derivation Function 2 (PBKDF2)) rather than creating a cryptographic hash of it. The latter makes a key susceptible to brute force or dictionary attacks.

Further, one or more initialization vectors may be used in encryption methods. An initialization vector will cause multiple copies of the same encrypted data to yield different cipher text output, preventing both replay and cryptanalytic attacks. This will also prevent an attacker from decrypting any data even with a stolen encryption key. Further, authentication then decryption may be used, wherein application data is decrypted only after the user has authenticated within the application. Another feature may relate to sensitive data in memory, which may be kept in memory (and not in disk) only when it's needed. For example, login credentials may be wiped from memory after login, and encryption keys and other data inside objective-C instance variables are not stored, as they may be easily referenced. Instead, memory may be manually allocated for these.

An inactivity timeout may be implemented, wherein after a policy-defined period of inactivity, a user session is terminated.

Data leakage from the application management framework 614 may be prevented in other ways. For example, if or when a managed application 610 is put in the background, the memory may be cleared after a predetermined (configurable) time period. When backgrounded, a snapshot may be taken of the last displayed screen of the application to fasten the foregrounding process. The screenshot may contain confidential data and hence should be cleared.

Another security feature may relate to the use of an OTP (one time password) 620 without the use of an AD (active directory) 622 password for access to one or more applications. In some cases, some users do not know (or are not permitted to know) their AD password, so these users may authenticate using an OTP 620 such as by using a hardware OTP system like SecurID (OTPs may be provided by different vendors also, such as Entrust or Gemalto). In some cases, after a user authenticates with a user ID, a text may be sent to the user with an OTP 620. In some cases, this may be implemented only for online use, with a prompt being a single field.

An offline password may be implemented for offline authentication for those managed applications 610 for which offline use is permitted via enterprise policy. For example, an enterprise may want StoreFront to be accessed in this manner In this case, the client agent 604 may require the user to set a custom offline password and the AD password is not used. Gateway server 606 may provide policies to control and enforce password standards with respect to the minimum length, character class composition, and age of passwords, such as described by the standard Windows Server password complexity requirements, although these requirements may be modified.

Another feature may relate to the enablement of a client side certificate for certain applications 610 as secondary credentials (for the purpose of accessing PKI protected web resources via the application management framework micro VPN feature). For example, a managed application 610 may utilize such a certificate. In this case, certificate-based authentication using ActiveSync protocol may be supported, wherein a certificate from the client agent 604 may be retrieved by gateway server 606 and used in a keychain. Each managed application 610 may have one associated client certificate, identified by a label that is defined in gateway server 606.

Gateway server 606 may interact with an enterprise special purpose web service to support the issuance of client certificates to allow relevant managed applications to authenticate to internal PKI protected resources.

The client agent 604 and the application management framework 614 may be enhanced to support obtaining and using client certificates for authentication to internal PKI protected network resources. More than one certificate may be supported, such as to match various levels of security and/or separation requirements. The certificates may be used by the Mail and Browser managed applications 610, and ultimately by arbitrary wrapped applications 610 (provided those applications use web service style communication patterns where it is reasonable for the application management framework to mediate HTTPS requests).

Application management client certificate support on iOS may rely on importing a public-key cryptography standards (PKCS) 12 BLOB (Binary Large Object) into the iOS keychain in each managed application 610 for each period of use. Application management framework client certificate support may use a HTTPS implementation with private in-memory key storage. The client certificate may not be present in the iOS keychain and may not be persisted except potentially in “online-only” data value that is strongly protected.

Mutual SSL or TLS may also be implemented to provide additional security by requiring that a mobile device 602 is authenticated to the enterprise, and vice versa. Virtual smart cards for authentication to gateway server 606 may also be implemented.

Another feature may relate to application container locking and wiping, which may automatically occur upon jail-break or rooting detections, and occur as a pushed command from administration console, and may include a remote wipe functionality even when a managed application 610 is not running.

A multi-site architecture or configuration of enterprise application store and an application controller may be supported that allows users to be serviced from one of several different locations in case of failure.

In some cases, managed applications 610 may be allowed to access a certificate and private key via an API (for example, OpenSSL). Trusted managed applications 610 of an enterprise may be allowed to perform specific Public Key operations with an application's client certificate and private key. Various use cases may be identified and treated accordingly, such as if or when an application behaves like a browser and no certificate access is required, if or when an application reads a certificate for “who am I,” if or when an application uses the certificate to build a secure session token, and if or when an application uses private keys for digital signing of important data (e.g. transaction log) or for temporary data encryption.

Protecting Sensitive Data Using Conversational History

As discussed above, aspects of the disclosure relate to protecting sensitive data using conversational history. For example, a first user and a second user may exchange messages (e.g., text messages) containing sensitive information via enterprise applications in a virtual workspace. In addition, one or more aspects of the disclosure may incorporate, be embodied in, and/or be implemented using one or more of the computer system architecture, remote-access system architecture, virtualized (hypervisor) system architecture, cloud-based system architecture, and/or enterprise mobility management systems discussed above in connection with FIGS. 1-6.

FIG. 7 depicts an illustrative computing environment for protecting sensitive data using conversational history in accordance with one or more illustrative aspects described herein. Referring to FIG. 7, computing environment 700 may include an enterprise data validation server 710, a workspace 720, a corpus database 730, a topic distribution database 740, an admin console device 750, user devices 760 and an enterprise network 770. Enterprise data validation server 710, virtual workspace 720, corpus database 730, topic distribution database 740, admin console device 750 and user devices 760 may include one or more physical components, such as one or more processors, memories, communication interfaces, and/or the like.

Enterprise data validation server 710 may include processor 711, memory 712, and communication interface 713. Processor 711 may execute instructions stored in memory 712 to cause enterprise data validation server 710 to perform one or more functions, such as automatically capture the conversation snippets and create a topic model. Communication interface 713 may include one or more network interfaces via which enterprise data validation server 710 can communicate with one or more other systems and/or devices in computing environment 700, such as, workspace 720, corpus database 730, topic distribution database 740, admin console device 750 and user devices 760. Memory 712 may store data associated with conversation snippets, topic models, topic distributions and bias values.

Enterprise data validation server 710 may be responsible for managing application execution, display and interaction and may send to and receive information from enterprise application 722 on workspace 720, corpus database 730, topic distribution database 740, admin console device 750, user devices 760 and other computing devices of computing environment 700. For example, enterprise data validation server 710 may be a server similar to management server 410.

Workspace 720 may be a software platform enabling enterprise users to remotely access and operate virtual desktops running in a cloud computing environment. Workspace 720 may include, for example, Citrix Workspace, Microsoft Teams, Skype and Google Workspace. Enterprise users may access enterprise application 722 executing in workspace 720. Enterprise application 722 may be one or more communication or collaboration tools that the enterprise users may use to communicate with each other. For example, enterprise application 722 may be a mail, browser or other wrapped applications 610 as illustrated in FIG. 6. Enterprise application 722 may be Software as a Service (SaaS) applications published by workspace 720. Enterprise application 722 may be instant messaging tools such as Microsoft Teams, Skype, communication platforms such as Slack or JIRA service management system, or collaboration tools such as Confluence platform.

Corpus database 730 may store conversation snippets from enterprise users in an organization. The conversation snippets may be filtered to remove stop words or be normalized before storing in corpus database 730. For example, a record in corpus database 730 may contain a pre-processed message corresponding to a conversation snippet between user A and user B, and a timestamp indicating when the conversation occurred. The pre-processed message may be pre-processed to remove those stop words from the conversation snippet and stop words may be words which do not add much meaning to the sentences, such as at, the, is, which, etc.

Topic distribution database 740 may store topic distributions between any pair of enterprise users. A conversation snippet may be a text block sent from one enterprise user to another. Each conversation snippet may have a topic distribution. The concept of the topic in the topic distribution may be similar to the topics of the news from a newspaper or a news website. For example, a piece of news about a sports star recently taking a role as an actor in a film may have two topics, entertainment and sport—with a distribution of 60% for entertainment, and 40% for sport. In another example, a conversation between two enterprise users who work as software developers may involve computer framework or coding skills, and the topics of their conversations may be labelled as “technology” or “coding.” A conversation between an engineer and his/her manager may have the topics such as “personal performance” or “career development.”

A topic may be an abstract concept used to measure how a conversation snippet belongs to different categories. For a given topic number k, if k=3, then there would be 3 topics, topic1, topic2 and topic3. For a conversation snippet C, the topic snippet may have the following distribution:

$\quad\left\{ \begin{matrix} {{C\left( {{topic}\; 1} \right)}\  = {70\%}} \\ {{C\left( {{topic}2} \right)}\  = {20\%}} \\ {{C\left( {{topic}3} \right)}\  = {10\%}} \end{matrix} \right.$

Where conversation snippet C may correspond to the topic distribution of 70% of topic 1, 20% of topic 2 and 10% of topic 3.

Another conversation snippet D may have the following topic distribution:

$\quad\left\{ \begin{matrix} {{D\left( {{topic}\; 1} \right)}\  = {10\%}} \\ {{D({topic2})}\  = {10\%}} \\ {{D({topic3})}\  = {80\%}} \end{matrix} \right.$

Where conversation snippet D may correspond to the topic distribution of 10% of topic 1, 10% of topic 2 and 80% of topic 3. Conversation snippets C and D may be originated from the same pair of enterprise users A and B, where conversation snippets C and D may occur between these users at different times on different subjects. Conversation snippets C and D may be originated from different pair of enterprise users. For example, conversation snippets C may correspond to conversation between enterprise user A and B, while conversation snippets D may correspond to conversation between enterprise users C and D. Topic distribution database 740 may store conversation snippets between a pair of enterprise users and the corresponding topic distribution.

Administrator console device 750 may have an administrator's console where enterprise administrators may send requests to enterprise validation server 710 to configure the threshold bias values based on the sensitivity of the content of the conversation among enterprise users and their roles in the organization via the administrator's console. Client devices 760 may be any type of computing device including, for example, a server, computer, laptop, tablet, smartphone, or other client device that includes a processor (e.g., computing device 201). User devices 760 may communicate, via their communication interfaces (e.g., wireless interfaces, LAN interfaces, WLAN interfaces), with other devices and/or entities such as enterpriser data validation server 710, as discussed in greater detail below. User devices 760 may also communicate with various network nodes described herein. For example, user devices 760 may execute enterprise application 722 on workspace 720. User devices 760 may communicate with each other and generate conversation data between a pair of enterprise users. User devices 760 may send conversation snippets to enterpriser data validation server 710 for processing, and may receive alerts from enterpriser data validation server 710 with a warning that the communication with a recipient contains sensitive information and asking the user to confirm the recipient.

Enterprise network 770 may include one or more wide area networks and/or local area networks and may interconnect one or more systems and/or devices included in computing environment 700. For example, enterprise network 770 may interconnect enterprise data validation server 710, workspace 720, corpus database 730, topic distribution database 740, admin console device 750 and user devices 760.

Enterprise data validation server 710 may monitor enterprise application 722 in workspace 720 and receive conversation snippets from enterprise application 722. For example, enterprise data validation server 710 may install plugins on user devices 760 to capture the conversation snippets using any suitable internet protocol, such as via HTTP messages in a browser. Enterprise data validation server 710 may install plugins on user devices 760 using native libraries or native codes to capture the conversation snippets such as text block sent from one enterprise user to another in any communication or collaboration tool, for example, Skype. Enterprise data validation server 710 may process the conversation snippets through filtering and normalization, and store the conversation snippets as pre-processed messages in corpus database 730. Entries in corpus database 730 may be used as inputs to build a topic model.

Enterprise data validation server 710 may build the topic model using any suitable machine learning model, such as LDA modeling based on the entries in corpus database 730. The topics produced by topic modeling techniques may be clusters of similar words. A topic model may be helpful to organize documents and offer insights to understand large collections of unstructured text messages in the conversation snippets. The topic model may capture the conversation snippets in a mathematical framework, which may allow examining a set of text blocks or conversation snippets and determining, based on the statistics of the words in each conversation snippet, the categories of topics and the distribution of the categories of topics in each conversation snippet. LDA may be a generative statistical model implementing machine learning algorithm using artificial intelligence. The topic model may receive the entries in corpus database 730 as training data and using the training data and a predefined K number as inputs to the topic model, where the K number represents the number of topics. The topic model may be tuned to identify an optimal K number. The tuned topic model may generate topic distributions between any two enterprise users and store the topic distributions in topic distribution database 740.

Enterprise data validation server 710 may continue to monitor messages in workspace 720 and detect a message being sent between enterprise users A and B which may contain sensitive information. Enterprise data validation server 710 may determine the topic distribution between users A and B based on the corresponding entries in topic distribution database 740. Enterprise data validation server 710 may determine a topic distribution of the message. Enterprise data validation server 710 may determine a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user, and send an alert to user A if the bias value exceeds a threshold indicating sensitive information may be disclosed to an unintended recipient. Enterprise data validation server 710 may prompt user A to respond to the alert and confirm the intended recipient for the message.

FIG. 8 depicts illustrative conversation snippets in accordance with one or more illustrative aspects described herein. In the example of FIG. 8, two enterprise users—namely idalko and bugsio, make conversation using the SaaS application such as a Confluence page published by a workspace, and the conversation data may be collected for analysis. Although the conversation data was captured in the Confluence page, similar conversations may occur within instant messaging tools such as Microsoft Teams, Skype, or communication platform such as Slack, or interactions in the ticketing of JIRA system. The conversation data captured in FIG. 8 includes two conversation snippets. As the conversation data indicates, their conversation is about framework/debugging skills, and the topic may be categorized as, for example, “technology” and “debugging.”

Through various enterprise applications in a workspace, an enterprise user or an employee may communicate with different colleagues at any given time of the workday. It may be a common practice for the enterprise user to keep multiple conversation windows open, and switch from one conversation window to another in order to chat with different people on different subjects. The enterprise user may switch from a first conversation window from a first enterprise application to a second conversation window of a second enterprise application to input or copy text or other contents to a recipient in the second conversation widow using the same or different enterprise application. It may often occur that the messages containing sensitive information or off-topic information may be sent inadvertently to unintended recipients.

For example, when a software engineer idalko may intend to query a test engineer bugsio on a question related to debugging the configuration setup, he may select one conversation window in a task bar where the test engineer is supposed to be on the other end of the conversation window, and click the test engineer's name displayed on the instant messaging tool e.g. Microsoft-Teams or Skype, then type the message and click “send.” However, the software engineer may select or open a conversation window where the recipient on the other end is actually his manager due to the fact that the names of his manger and the test engineer are similar (both with the first name John), which may lead to potentially embarrassing situation to send off-topic information to unintended recipient. This may lead to more serious consequences when certain enterprise users may have access to sensitive information, such as financial information or personnel information in a Human Resources division. There is a risk that the enterprise users may send messages to unintended recipients by mistake, and unauthorized personnel may gain access to sensitive information that may lead to unanticipated data leakage.

The enterprise data validation server may determine a first topic distribution between enterprise users idalko and bugsio based on a topic model. The enterprise data validation server may also determine a second topic distribution of the message/conversion snippets of FIG. 8. The enterprise data validation server may further determine a bias associated with the message by comparing the first topic distribution and the second topic distribution. If the bias exceeds a threshold value, indicating the message to be a great deviation from previous conversational history between the users, the enterprise data validation server may send the user idalko an alert including a warning message. As such, the enterprise data validation server may provide a solution identifying whether the person on the other end of the communication is the intended party to receive the message before the message is being sent in a workspace environment.

FIGS. 9A-9C depict an example event sequence for protecting sensitive data using conversational history, in accordance with one or more illustrative aspects described herein. The communications between components of FIGS. 9A-9C may be encrypted via Transport Layer Security (TLS) cryptographic protocols or Internet Protocol Security (IPsec) tunnels that provide communications security over a computer network.

Referring to FIG. 9A, at step 901, enterprise data validation server may configure enterprise applications to be monitored for conversation snippets. Enterprise users may access a workspace via user devices 760, where the workspace may be a virtual environment hosting applications, content and devices associated with an organization. For example, Citrix Workspace is a digital workspace software platform that may allow multiple enterprise users to remotely access and operate Microsoft Windows desktops running in a datacenter or a public or private cloud, via user devices. Enterprise users may access virtual desktops and applications through Citrix Workspace wrapped applications that delivered and managed as managed applications of 610 in FIG. 6. Google Workspace may include collaboration tools such as Gmail, Calendar, Meet, Chat, Drive, Docs, Sheets, Slides, Forms and Sites. A workspace may also include instant messaging tools such as Microsoft Teams, Skype, communication platform such as Slack, JIRA ticketing platform or Confluence pages.

In some examples, enterprise data validation server 710 may automatically detect the communication or collaboration tools in a workspace to be monitored by enterprise data validation server 710. Enterprise data validation server 710 may also set up enterprise applications 722 to be monitored via admin console device 750 that may communicate with enterprise data validation server 710. For example, an enterprise administrator may access the admin console, select the enterprise applications, such as a mail application, a chat application such as Skype, and a collaboration application such as Microsoft Teams to be monitored.

In some examples, enterprise data validation server 710 may monitor enterprise application 722 via a plugin or an extension to an embedded browser in the workspace. As one enterprise user communicates with another, the browser extension may cache the conversation snippet in a local cache. The plugin or the browser extension may also capture the conversation snippets in the HTTP message. For example, the browser extension may capture the conversation snippets from content of the DOM elements. Enterprise data validation server 710 may further monitor enterprise application 722 via a plugin to the native library or native codes of enterprise application 722. For example, enterprise data validation server 710 may capture the conversation snippets via the plugin to the native codes of Skype.

At step 902, enterprise data validation server may receive the conversation snippets from the enterprise applications. For example, the plugin or browser extension may send the conversation snippets captured in at step 901 to enterprise data validation server 710. The plugin or browser extension may send the conversation snippets from the local cache to enterprise data validation server 710.

At step 903, enterprise data validation server may process the conversation snippets. Enterprise data validation server 710 may process the conversation snippets and remove the stop words from the conversation snippets. The stop words may be words that do not add much meaning to the sentences, such as the words “at, the, is, which,” etc. The stop words are the most common words and enterprise data validation server 710 may eliminate these words for the purposes of saving space and time in processing of large amount of conversation data. Enterprise data validation server 710 may filter the stop words using, for example, term frequency—inverse document frequency (TFIDF), which may be a numerical statistic model that may reflect how important a word is to a document in a collection or corpus. In the example of FIG. 8, there are two conversation snippets between enterprise users idalko and bugsio. Enterprise data validation server 710 may filter out certain stop words “that's, the, are, to” to adjust for the fact that some words may appear more frequently in general; while assign a larger weight factor to certain key words such as “debug” or “config.”

At step 904, enterprise data validation server may send the conversation snippets to the corpus database. After the preprocessing of the conversation snippets, enterprise data validation server 710 may generate preprocessed messages corresponding to the conversation snippets. Enterprise data validation server 710 may send the conversation snippets and preprocessed messages to corpus database 730.

At step 905, the corpus database may store the conversation snippets. In some examples, preprocessed conversation snippets from a workspace may be collected and stored in corpus database 730. An enterprise may implement an IT policy to select certain groups of enterprise users to opt-in this data storage feature. For example, the IT policy may stipulate that for enterprise users who work for certain department, have certain managerial rolls or have access to sensitive data, their conversation data may be stored in corpus database 730. An IT policy may also stipulate that the most-current data, such as conversation snippets generated in the past 3 or 6 months may be stored in corpus database 730.

A record in corpus database 730 may include the names of the enterprise users, the preprocessed message corresponding to the conversation snippet, and the timestamp indicating when the conversation occurred. For example, corpus database 730 may store a record corresponding to the conversation snippets of FIG. 8 captured in Confluence, which may include the data fields of “idalko,” “bugsio,” preprocessed message, and a timestamp (“5 days ago”).

At step 906, enterprise data validation server may retrieve the conversation snippets from the corpus database. At step 907, enterprise data validation server 710 may build a topic model. Enterprise data validation server 710 may retrieve the conversation snippet data from corpus database 730 and use the conversation snippet data to build a topic model. Topic models may be referred to as probabilistic topic models, which may use statistical algorithms for discovering the latent semantic structures of an extensive text body such as the records in corpus database 730. The amount of conversation data generated from enterprise users in a workspace may pose a challenge to the processing capacity of an organization. A topic model may be helpful to organize the conversation snippets and offer insights to understand large collections of unstructured text messages such as the conversation snippets in corpus database 730. The topic model may capture the conversation snippets in a mathematical framework, which may allow examining a set of text blocks or conversation snippets and determining, based on the statistics of the words in each conversation snippets, the categories of topics and the distribution of the categories of topics in each conversation snippet.

A topic may be an abstract concept used to measure how a conversation snippet belongs to different categories. For a given topic number k, if k=3, then there would be 3 topics, topic1, topic2 and topic3. For example, in the example of FIG. 8, the first conversation snippet may have the topic distribution of 70% of topic 1, 20% of topic 2 and 10% of topic 3. The second conversation snippet may have the topic distribution of 10% of topic 1, 10% of topic 2 and 80% of topic 3.

In some examples, enterprise data validation server 710 may use a suitable machine learning model such as LDA modeling to build the topic model. LDA may be a generative statistical model implementing machine learning algorithm to infer a set of topics over a collect of content. LDA modeling may allow sets of observations to be explained by unobserved groups and explain why some parts of the data are similar. For example, if observations are words collected into conversation snippets, each conversation snippet may correspond to a plurality of topics and that each word's presence may be attributable to one of the conversation snippet's topics. Enterprise data validation server 710 may use LDA modeling to automatically classify the conversation snippets and estimate their relevance to various topics. In LDA modeling, each conversation snippet may be viewed as a mixture of various topics, where each conversation snippet may be considered to have a set of topics that are assigned to it via LDA.

In some examples, enterprise data validation server 710 may use probabilistic latent semantic analysis (pLSA) modeling to build the topic model. pLSA may be similar to LDA under a uniform Dirichlet prior distribution; however in LDA, the topic distribution may be assumed to have a sparse Dirichlet prior. The sparse Dirichlet priors encode the intuition that the conversation snippets may cover only a small set of topics and that topics may use only a small set of words frequently. In practice, LDA modeling may result in a better disambiguation of words and a more precise assignment of the conversation snippets to topics.

The topic model may receive the entries in corpus database 730 as training data and using the training data and a predefined K number as inputs to the topic model, where the K number represents the number of topics.

At step 908, enterprise data validation server may tune the topic model. The topic model may be tuned to identify an optimal K number. In some examples, enterprise data validation server 710 may use a practical approach based on quantitative measures such as log-likelihood or perplexity to identify the optimal K number. The log-likelihood method may measure the plausibility of model parameters given the conversation snippets, while the perplexity method may measure model's surprise given the conversation snippets. Enterprise data validation server 710 may fit the model for several values of K number and tune the model based on factors such as the number of conversation snippets, the number of words or terms and the number of iterations.

In some examples, enterprise data validation server 710 may tune the model using empirical data. For example, enterprise data validation server 710 may apply the topic model into a production environment or a particular department within the organization. As explained in later steps, alerts may be sent to enterprise users based on a determined bias value. Based on user feedback on the preprocessed messages and their response to the alerts, enterprise data validation server 710 may tune the value of the K number of topics, identify what words or terms may impact the accuracy of the model, and remove such words or terms from the model.

Turning to FIG. 9B, at step 909, the enterprise data validation server may determine the topic distribution between any two users. Enterprise data validation server 710 may use the tuned model to determine topic distributions between any two enterprise users A and B. Suppose there are totally N conversation snippets between users A and B: C₁, C₂, C₃, . . . C_(N), and suppose topic number is K, then for each C_(i), its topic distribution is

$\begin{matrix} \left\{ {{{\begin{matrix} C_{i1} \\ C_{i2} \\ \ldots \\ C_{iK} \end{matrix}1} \leq i \leq {N{the}\mspace{14mu}{topic}\mspace{14mu}{distribution}\mspace{14mu}{between}\mspace{14mu} A\mspace{14mu}{and}\mspace{14mu} B\mspace{14mu}{is}T{D\left( {A,B} \right)}}} = \left\{ {{\begin{matrix} C_{{({A,B})}1} \\ C_{{({A,B})}2} \\ \ldots \\ C_{{({A,B})}K} \end{matrix}{Each}\mspace{14mu} C_{{({A,B})}j}} = \frac{\sum\limits_{i = 1}^{N}C_{ij}}{N}} \right.} \right. & {{Formula}\mspace{14mu} 1} \end{matrix}$

When the LDA model is denoted as M, M fits best for those conversation snippet data that is used to generate M. M may be used to infer the topic distribution for a given conversation snippet. Subsequently, for any two enterprise users A and B, enterprise data validation server 710 may use M to infer all conversation snippet topic distribution for all messages generated during their historical conversation, and determine the topic distribution between A and B TD(A,B) using Formula 1.

At step 910, enterprise data validation server may send the topic distributions and the tuned topic model to the topic distribution database. At step 911, topic distribution database may store the topic distributions and the tuned topic model. Enterprise data validation server 710 may send tuned model M and the topic distributions between any two users in the historical conversation to topic distribution database 740, and topic distribution database 740 may store the topic distribution data and the tuned model M.

At step 912, enterprise data validation server may detect a message being sent from user A to user B. In some examples, enterprise data validation server 710 may detect that the message may contain sensitive information based on the users' affiliation with certain departments, for example, a finance or HR department within the organization. Enterprise data validation server 710 may detect that the message may contain sensitive information based on a determination that the users have certain privilege to access sensitive information or have certain managerial roles. Enterprise data validation server 710 may detect that the message may contain sensitive information based on some key words in the message that may indicate sensitive information. For example, as indicated in the two conversation snippets of FIG. 8, users idalko and bugsio previously engaged in conversations related to “technology” or “debugging.” Enterprise data validation server 710 may detect that user idalko attempts to send user bugsio a new message related to “performance review” and these key words may indicate sensitive information. Enterprise data validation server 710 may delay the delivery of the message to user bugsio for further processing.

At step 913, enterprise data validation server may retrieve the topic distribution between users A and B. Enterprise data validation server 710 may retrieve the record associated with the topic distribution between users A and B from topic distribution database 740. Enterprise data validation server 710 may also retrieve the tuned topic model M from topic distribution database 740.

At step 914, enterprise data validation server may determine a topic distribution of the message. For example, after detecting the new message is being sent between users A and B, enterprise data validation server 710 may use the tuned topic model M to infer topic distribution of the new message, denoted as X,

$X = \left\{ \begin{matrix} X_{1} \\ X_{2} \\ \ldots \\ X_{K} \end{matrix} \right.$

At step 915, enterprise data validation server may determine a bias associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user. Enterprise data validation server 710 may determine a bias between X and the topic TD(A,B) as follow:

$\begin{matrix} {{BIAS} = \sqrt{\sum\limits_{i = 1}^{K}\left( {X_{i} - C_{{({A,B})}i}} \right)^{2}}} & {{Formula}\mspace{14mu} 2} \end{matrix}$

Where TD(A,B) denotes the topic distribution between users A and B.

In some examples, when user A is sending a message to user B through, for example, a SaaS application published in the workspace, enterprise data validation server 710 may first uses the tuned LDA model generated to determine its topic distribution TD(A,B), the topic distribution of the message X, and then determine the bias against the existing topic distribution of all historical conversations between users A and B using Formula 2.

A bias may indicate a distance of the new message from the historical conversations between two users. A large bias value may indicate that the new message may not be relevant to previous conversations. For example, a new message related to “performance review” may not be relevant to previous conversations between users idalko and bugsio.

In some examples, enterprise data validation server 710 may assign a weigh factor to a bias value. For example, if the conversation contains any predefined sensitive words, enterprise data validation server 710 may assign the bias with a weight factor such as 1.1 or 1.2.

At step 916, enterprise data validation server may compare the bias with a threshold value. If the bias exceeds a predefined threshold value, the message being sent may be recognized as irregular, it may not be divulged in the conversations between user A and B. Enterprise data validation server 710 may take proper action to block it or prompt the sender a warning asking the sender to confirm such operation.

In some examples, enterprise data validation server 710 may adjust the bias based on the department affiliation of the enterprise users. For example, when users A and B are employees from a finance or HR department who have access to sensitive data, a determination that the bias has exceeded the threshold may generate a warning. In this situation, the threshold may be defined in an aggressive way to minimize chances of false negative. When user A and B are employees from an engineering department who have access to less sensitive data, there may be less concerns for the data leakage. The threshold for the engineering department may be set to be a higher value to minimize the interruptions to normal communications. In some examples, an administrator may use admin console 750 to configure and adjust the threshold value. The threshold value may be set to be lower for highly sensitive data and to be higher for less sensitive data.

At step 917, enterprise data validation server may generate an alert to the sender of the message after a determination that the bias has exceeded the threshold. The alert may contain a warning message and ask the sender to confirm the intended recipient.

At step 918, enterprise data validation server may determine whether a confirmation is received from the sender.

At step 919, if a confirmation is received from the sender, enterprise data validation server 710 may permit the message being sent from user A to user B.

At step 920, if a confirmation is not received from the sender, enterprise data validation server 710 may block the message being sent from user A to user B.

FIG. 10 depicts an example algorithm of protecting sensitive data using conversational history in accordance with one or more illustrative aspects described herein. Referring to FIG. 10, at step 1002, an enterprise data validation server (e.g. enterprise data validation server 710) having at least one processor, a communication interface, and memory, may receive, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment. At step 1004, the enterprise data validation server may create, based on the conversation snippets, a topic model using a machine learning model, such as latent Dirichlet allocation (LDA) modeling. At step 1006, the enterprise data validation server may detect, via the communication interface, a message is being sent from a first user to a second user, the message may contain potentially sensitive or off-topic data. At step 1008, the enterprise data validation server may determine, based on the topic model, a topic distribution between the first user and the second user. At step 1009, the enterprise data validation server may determine a topic distribution of the message. At step 1010, the enterprise data validation server may determine a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user. Accordingly, at step 1012, after a determination that the bias value exceeds a threshold, the enterprise data validation server may send an alert to the first user to prevent the message being sent to the second user.

The following paragraphs M1 through M11 describe examples of methods that may be implemented in accordance with the present disclosure.

(M1) A method comprising: at an enterprise data validation server comprising at least one processor, memory, and a communication interface: receiving, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment; creating, based on the conversation snippets, a topic model using a machine learning model, such as latent Dirichlet allocation (LDA) modeling; detecting, via the communication interface, a message is being sent from a first user to a second user; determining, based on the topic model, a topic distribution between the first user and the second user; determining a topic distribution of the message; determining a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user; and based on a determination that the bias value exceeds a threshold, sending an alert to the first user to prevent the message being sent to the second user.

(M2) A method may be performed as described in paragraph (M1) further comprising: prior to receiving the conversation snippets, capturing HTTP message associated with the conversation snippets from a browser of an application of the plurality of the applications.

(M3) A method may be performed as described in paragraph (M1) further comprising: prior to receiving the conversation snippets, capturing the conversation snippets from native codes of an application of the plurality of the applications.

(M4) A method may be performed as described in any of paragraphs (M1) through (M3) further comprising: after receiving the conversation snippets, storing the conversation snippets in a corpus database.

(M5) A method may be performed as described in any of paragraphs (M1) through (M4) further comprising: after creating the topic model, generating the topic distribution associated with conversations associated with a plurality of users; and storing the topic distribution in a topic distribution database.

(M6) A method may be performed as described in any of paragraphs (M1) through (M5) further comprising: using the topic distribution and a topic number as inputs for the LDA modeling.

(M7) A method may be performed as described in any of paragraphs (M1) through (M6) further comprising: tuning the topic model based to identify an appropriate topic number.

(M8) A method may be performed as described in any of paragraphs (M1) through (M7) further comprising: applying the topic model to a production environment; and tuning the topic model based on user feedback.

(M9) A method may be performed as described in any of paragraphs (M1) through (M8) further comprising: adjusting the bias value based on sensitive information being in the message.

(M10) A method may be performed as described in any of paragraphs (M1) through (M9) further comprising: sending the alert indicating that the sensitive information in the message is to be disclosed to the second user; and blocking the message from being sent to the second user based on a negative feedback from the first user.

(M11) A method may be performed as described in any of paragraphs (M1) through (M9) further comprising: sending the alert indicating that the sensitive information in the message is to be disclosed to the second user; and permitting the message being sent to the second user based on a positive feedback from the first user.

The following paragraphs A1 through A11 describe examples of apparatuses that may be implemented in accordance with the present disclosure.

(A1) An apparatus comprising at least one processor, a communication interface and memory storing instructions that, when executed by the at least one processor, cause the apparatus to: receive, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment; creating, based on the conversation snippets, a topic model using a machine learning model, such as latent Dirichlet allocation (LDA) modeling; detecting, via the communication interface, a message is being sent from a first user to a second user; determining, based on the topic model, a topic distribution between the first user and the second user; determining a topic distribution of the message; determining a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user; and based on a determination that the bias value exceeds a threshold, sending an alert to the first user to prevent the message being sent to the second user.

(A2) An apparatus as described in paragraph (A1), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: prior to receiving the conversation snippets, capture HTTP message associated with the conversation snippets from a browser of an application of the plurality of the applications.

(A3) An apparatus as described in paragraph (A1), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: prior to receiving the conversation snippets, capture the conversation snippets from native codes of an application of the plurality of the applications.

(A4) An apparatus as described in any of paragraphs (A1) through (A3), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: after receiving the conversation snippets, store the conversation snippets in a corpus database.

(A5) An apparatus as described in any of paragraphs (A1) through (A4), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: after creating the topic model, generate the topic distribution associated with conversations associated with a plurality of users; and store the topic distribution in a topic distribution database.

(A6) An apparatus as described in any of paragraphs (A1) through (A5), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: use the topic distribution and a topic number as inputs for the LDA modeling.

(A7) An apparatus as described in any of paragraphs (A1) through (A6), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: tune the topic model based to identify an appropriate topic number.

(A8) An apparatus as described in any of paragraphs (A1) through (A7), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: apply the topic model to a production environment; and tuning the topic model based on user feedback.

(A9) An apparatus as described in any of paragraphs (A1) through (A8), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: adjust the bias value based on sensitive information being in the message.

(A10) An apparatus as described in any of paragraphs (A1) through (A9), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: send the alert indicating that the sensitive information in the message is to be disclosed to the second user; and block the message from being sent to the second user based on a negative feedback from the first user.

(A11) An apparatus as described in any of paragraphs (A1) through (A9), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the apparatus to: send the alert indicating that the sensitive information in the message is to be disclosed to the second user; and permit the message being sent to the second user based on a positive feedback from the first user.

The following paragraphs (CRM1) through (CRM11) describe examples of computer-readable media that may be implemented in accordance with the present disclosure.

(CRM1) One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to: receive, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment; creating, based on the conversation snippets, a topic model using a machine learning model, such as latent Dirichlet allocation (LDA) modeling; detecting, via the communication interface, a message is being sent from a first user to a second user; determining, based on the topic model, a topic distribution between the first user and the second user; determining a topic distribution of the message, determining a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user; and based on a determination that the bias value exceeds a threshold, sending an alert to the first user to prevent the message being sent to the second user.

(CRM2) One or more non-transitory computer-readable media as described in paragraph (CRM1), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: prior to receiving the conversation snippets, capture HTTP message associated with the conversation snippets from a browser of an application of the plurality of the applications.

(CRM3) One or more non-transitory computer-readable media as described in paragraph (CRM1), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: prior to receiving the conversation snippets, capture the conversation snippets from native codes of an application of the plurality of the applications.

(CRM4) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM3), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: after receiving the conversation snippets, store the conversation snippets in a corpus database.

(CRM5) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM4), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: after creating the topic model, generate the topic distribution associated with conversations associated with a plurality of users; and store the topic distribution in a topic distribution database.

(CRM6) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM5), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: use the topic distribution and a topic number as inputs for the LDA modeling.

(CRM7) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM6), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: tune the topic model based to identify an appropriate topic number.

(CRM8) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM7), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: apply the topic model to a production environment; and tuning the topic model based on user feedback.

(CRM 9) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM8), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: adjust the bias value based on sensitive information being in the message.

(CRM10) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM9), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: send the alert indicating that the sensitive information in the message is to be disclosed to the second user; and block the message from being sent to the second user based on a negative feedback from the first user.

(CRM11) One or more non-transitory computer-readable media as described in any of paragraphs (CRM1) through (CRM9), wherein the memory stores additional instructions that, when executed by the at least one processor, cause the computing platform to: send the alert indicating that the sensitive information in the message is to be disclosed to the second user; and permit the message being sent to the second user based on a positive feedback from the first user.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are described as example implementations of the following claims. 

What is claimed is:
 1. A method comprising: at an enterprise data validation server comprising at least one processor, memory, and a communication interface: receiving, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment; creating, based on the conversation snippets, a topic model using machine learning modeling; detecting, via the communication interface, a message is being sent from a first user to a second user; determining, based on the topic model, a topic distribution between the first user and the second user; determining a topic distribution of the message; determining a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user; adjusting the bias value based on sensitive information being in the message; and based on a determination that the bias value exceeds a threshold, sending an alert to the first user to prevent the message being sent to the second user.
 2. The method of claim 1, wherein the machine learning modeling comprises latent Dirichlet allocation (LDA) modeling.
 3. The method of claim 1, further comprising: prior to receiving the conversation snippets, capturing a HTTP message associated with the conversation snippets from a browser of an application of the plurality of the applications.
 4. The method of claim 1, further comprising: prior to receiving the conversation snippets, capturing the conversation snippets from native codes of an application of the plurality of the applications.
 5. The method of claim 1, further comprising: after receiving the conversation snippets, storing the conversation snippets in a corpus database.
 6. The method of claim 1, further comprising: after creating the topic model, generating the topic distribution associated with conversations associated with a plurality of users; and storing the topic distribution in a topic distribution database.
 7. The method of claim 1, further comprising: using the topic distribution and a topic number as inputs for the machine learning modeling.
 8. The method of claim 1, further comprising: tuning the topic model to identify an appropriate topic number.
 9. The method of claim 1, further comprising: applying the topic model to a production environment; and tuning the topic model based on user feedback.
 10. The method of claim 1, further comprising: sending the alert indicating that the sensitive information in the message is to be disclosed to the second user; and blocking the message from being sent to the second user based on a negative feedback from the first user.
 11. The method of claim 1, further comprising: sending the alert indicating that the sensitive information in the message is to be disclosed to the second user; and permitting the message being sent to the second user based on a positive feedback from the first user.
 12. An enterprise data validation server comprising: at least one processor; a communication interface; memory storing instructions that, when executed by the at least one processor, cause the enterprise data validation server to: receive, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment; create, based on the conversation snippets, a topic model using machine learning modeling; detect, via the communication interface, a message is being sent from a first user to a second user; determine, based on the topic model, a topic distribution between the first user and the second user; determine a topic distribution of the message; determine a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user; adjust the bias value based on sensitive information being in the message; and based on a determination that the bias value exceeds a threshold, send an alert to the first user to prevent the message from being sent to the second user.
 13. The enterprise data validation server of claim 12, wherein the memory stores additional instructions that, when executed by the at least one processor, cause the enterprise data validation server to: create the topic model using latent Dirichlet allocation (LDA) modeling.
 14. The enterprise data validation server of claim 12, wherein the memory stores additional instructions that, when executed by the at least one processor, cause the enterprise data validation server to: prior to receiving the conversation snippets, capture HTTP message associated with the conversation snippets from a browser of an application of the plurality of the applications.
 15. The enterprise data validation server of claim 12, wherein the memory stores additional instructions that, when executed by the at least one processor, cause the enterprise data validation server to: prior to receiving the conversation snippets, capture the conversation snippets from native codes of an application of the plurality of the applications.
 16. The enterprise data validation server of claim 12, wherein the memory stores additional instructions that, when executed by the at least one processor, cause the enterprise data validation server to: after creating the topic model, generate the topic distribution associated with conversations associated with a plurality of users; and storing the topic distribution in a topic distribution database.
 17. The enterprise data validation server of claim 12, wherein the memory stores additional instructions that, when executed by the at least one processor, cause the enterprise data validation server to: using the topic distribution and a topic number as inputs for the machine learning modeling.
 18. The enterprise data validation server of claim 12, wherein the memory stores additional instructions that, when executed by the at least one processor, cause the enterprise data validation server to: tune the topic model to identify an appropriate topic number.
 19. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to: receive, via the communication interface, conversation snippets associated with a plurality of applications in a virtual computing environment; create, based on the conversation snippets, a topic model using machine learning modeling; detect, via the communication interface, a message is being sent from a first user to a second user; determine, based on the topic model, a topic distribution between the first user and the second user; determine a topic distribution of the message; determine a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user; adjust the bias value based on sensitive information being in the message; and based on a determination that the bias value exceeds a threshold, send an alert to the first user to prevent the message being sent to the second user. 